Seminar 6 (og 7)

SOK-2011

Author

Andrea Mannberg

På dette seminaret skal vi sammenligne tre mål på bærekraftig utvikling: - Den justerte nettosparingsraten (genuine savings, GSI) - “Social Progress Index” (SPI) alternativt “Sustainable Society Index” (SSI) som bygger på FN sine bærekraftsmål - Human Development Index - original og justert for klima og miljø, og for ulikhet.

Oppgavene består i det å først visualisere utviklingen i måleinstrumentene over tid for ulike land, og deretter diskutere fordeler og ulemper med de ulike måleinstrumentene.

Dokumentasjon:

Pakken SpiR (NB: Toks vekk fra CRAN 5 mars 2023)
Generell info om SPI
Informasjon om SSI Wikipedia, TH Köln
HDI og PHDI (HDI ajdusted for planetary pressures)

Data:

Data til SSI
HDI justert for skader på miljøet og for ulikhet:
- Tverrsnittsdata:
  - phdi - HDI justert for skader på miljøet
  - ihdi - HDI justert for ulikhet).
- Paneldata for HDI justert for skader på miljøet og for ulikhet

Pakker:

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(plyr))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(WDI))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(countrycode))
suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(ggrepel))
suppressPackageStartupMessages(library(spiR))
suppressPackageStartupMessages(library(readxl))
suppressPackageStartupMessages(library(readr))

Oppgave 1

Diskuter:

Hvorfor skal vi måle “bærekraftig utvikling”?
Hva ønsker vi få ut av et “godt” måleinstrument?
Hva kan/skal vi bruke resultatene (og måleinstrumentene) til?

Oppgave 2

Visualiser utviklingen over tid for et utvalg av land i en graf. Lagre grafen, slik at du kan ta den fram seinere

Du kan selv velge hvilke land du vil se på. Et forslag er å se på følgende land (landkode i parentes):

-   Verden (WLD)
-   USA (USA)
-   Norge (NOR)
-   China (CHN)
-   Brasilia (BRA)
-   Russland (RUS)
-   Nigeria (NGA)

GSI finns beregnet i WDI, du finner variabelnavnet her nede.

# NY.ADJ.SVNG.GN.ZS       Adjusted savings: national savings (% of GNI) - capital consumption + public expenditure on education - natural resource depletion - damage from emissions of particulate matter - damage from CO2 emissions

Oppgave 2: Løsning

df_sdg0<-WDI(
  country = c("WLD","USA", "NOR", "NGA", "BRA", "CHN", "RUS"),
  indicator = c('gsi'="NY.ADJ.SVNG.GN.ZS"),  
  start = 1999,
  end = 2019,
  extra = TRUE, 
  cache = NULL,
  latest = NULL,
  language = "en"
)

df_sdg <- subset(df_sdg0, select = c(country, iso3c, year, gsi) ) 
df_sdg <-  df_sdg %>% mutate_all(na_if,"")
df_sdg = df_sdg %>%  
  mutate(year= as.numeric(year))

p_gsi<- ggplot(data=df_sdg,aes(x=year,y=gsi, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="Justert sparerate (netto)")  +
  geom_hline(yintercept=0, size=1, color = "red")

p_gsi

Oppgave 3

\(SPI\): Gjeometrisk gjennomsnitt av tre dimensjoner:

\[ SPI = (Basic Human Needs)^{\frac{1}{3}}\cdot(FoundationsofWellbeing)^{\frac{1}{3}}\cdot(Opportunity)^{\frac{1}{3}} \]

Basic Human needs (gjeometrisk gjennomsnitt):
- Nutrition and Basic medical care
- Water and sanitation
- Shelter
- Personal safety
Foundations of wellbeing (gjeometrisk gjennomsnitt):
- Access to basic knowledge
- Access to information and communication
- Health and wellness
- Environmental quality
Opportunity(gjeometrisk gjennomsnitt):
- Personal rights
- Personal freedom and choice
- Inclusiveness
- Access to advanced education

\(SSI\): Tre mål:

Human wellbeing
- Basic Human needs: Sufficient food, Sufficent drink, Safe sanitation
- Personal Development and Health: Education, Healthy life, Gender equality
- Well-balanced society: Income distribution, Population growth, Good governance

\[ SSI_{HB} = (Basic Human Needs)^{\frac{1}{3}}\cdot(Personal Development And Health)^{\frac{1}{3}}\cdot(Wellbalanced society)^{\frac{1}{3}} \]

Environmental wellbeing
- Natural resources: Biodiversity, Renewable water resources, consumption
- Climate and Energy: Energy use, Grennhouse gases, Renewable energy

\[ SSI_{NB} = (Natural Resources)^{\frac{1}{2}}\cdot(Climate And Energy)^{\frac{1}{2}} \]

Economic wellbeing
- Transition: Organic farming, Genuine savings
- Economy: GDP, Employment, Public debt

\[ SSI_{EB} = (Transition)^{\frac{1}{2}}\cdot(Economy)^{\frac{1}{2}} \]

Oppgave 3

Visualiser utviklingen i SPI over tid for samme utvalg av land som du brukte til oppgave 1 (i spiR er koden for verden “WWW”). Bruk de Årene som er tilgjengelige.
Lagre grafen, slik at du kan ta den fram seinere

Du laster inn data fra spiR ved bruk av koden her nede:

df_spir<- spir_data(country = c("WWW", "USA", "NOR", "NGA", "CHN", "BRA", "RUS"), indicators="SPI", )

SPI er allerede beregnet i datasettet. Du kan se de indikatorer som inngC%r i SPI ved C% bruke koden her nede:

myIndicator <- spir_indicator()

Oppgave 3: Løsning spiR

df_spir<- spir_data(country = c("WWW", "USA", "NOR", "NGA", "CHN", "BRA", "RUS"), indicators="SPI", )

df_spir = df_spir %>%  
  mutate(year = as.numeric(year))

p_spi<- ggplot(data=df_spir,aes(x=year,y=value, group=country_name, color=country_name)) + 
  geom_line(aes(group=country_name), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="Social Progress Index")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_spir %>% group_by(country_name) %>%
      filter(year==max(year)),
    aes(year, value, fill = factor(value), label = sprintf('%6.2g', value)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_spir %>% group_by(country_name) %>%
      filter(year==min(year)),
    aes(year, value, fill = factor(value), label = sprintf('%6.2g', value)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c(2010, 2012, 2014, 2016, 2018, 2020 ))+
  theme_classic()

p_spi

myIndicator <- spir_indicator()
myIndicator$indicator_name

 [1] "Social Progress Index"                                                                 
 [2] "Basic Human Needs"                                                                     
 [3] "Foundations of Wellbeing"                                                              
 [4] "Opportunity"                                                                           
 [5] "Nutrition and Basic Medical Care"                                                      
 [6] "Water and Sanitation"                                                                  
 [7] "Shelter"                                                                               
 [8] "Personal Safety"                                                                       
 [9] "Access to Basic Knowledge"                                                             
[10] "Access to Information and Communications"                                              
[11] "Health and Wellness"                                                                   
[12] "Environmental Quality"                                                                 
[13] "Personal Rights"                                                                       
[14] "Personal Freedom and Choice"                                                           
[15] "Inclusiveness"                                                                         
[16] "Access to Advanced Education"                                                          
[17] "Undernourishment (% of pop.)"                                                          
[18] "Maternal mortality rate (deaths/100,000 live births)"                                  
[19] "Child mortality rate (deaths/1,000 live births)"                                       
[20] "Child stunting (% of children)"                                                        
[21] "Deaths from infectious diseases (deaths/100,000 people)"                               
[22] "Access to at least basic drinking water (% of pop.)"                                   
[23] "Access to piped water (% of pop.)"                                                     
[24] "Access to at least basic sanitation facilities (% of pop.)"                            
[25] "Rural open defecation (% of pop.)"                                                     
[26] "Access to electricity (% of pop.)"                                                     
[27] "Quality of electricity supply (1=low; 7=high)"                                         
[28] "Household air pollution attributable deaths (deaths/100,000 people)"                   
[29] "Access to clean fuels and technology for cooking (% of pop.)"                          
[30] "Homicide rate (deaths/100,000 people)"                                                 
[31] "Perceived criminality (1=low; 5=high)"                                                 
[32] "Political killings and torture (0=low freedom; 1=high freedom)"                        
[33] "Traffic deaths (deaths/100,000 people)"                                                
[34] "Adult literacy rate (% of pop. aged 15+)"                                              
[35] "Primary school enrollment (% of children)"                                             
[36] "Secondary school enrollment (% of children)"                                           
[37] "Gender parity in secondary enrollment (distance from parity)"                          
[38] "Access to quality education (0=unequal; 4=equal)"                                      
[39] "Mobile telephone subscriptions (subscriptions/100 people)"                             
[40] "Internet users (% of pop.)"                                                            
[41] "Access to online governance (0=low; 1=high)"                                           
[42] "Media censorship (0=frequent; 4=rare)"                                                 
[43] "Life expectancy at 60 (years)"                                                         
[44] "Premature deaths from non-communicable diseases (deaths/100,000 people)"               
[45] "Access to essential services(0=none; 100=full coverage)"                               
[46] "Access to quality healthcare (0=unequal; 4=equal)"                                     
[47] "Outdoor air pollution attributable deaths (deaths/100,000 people)"                     
[48] "Greenhouse gas emissions (CO2 equivalents/GDP)"                                        
[49] "Biome protection"                                                                      
[50] "Political rights (0=no rights; 40=full rights)"                                        
[51] "Freedom of expression (0=no freedom; 1=full freedom)"                                  
[52] "Freedom of religion (0=no freedom; 4=full freedom)"                                    
[53] "Access to justice (0=non-existent; 1=observed)"                                        
[54] "Property rights for women (0=no rights; 5=full rights)"                                
[55] "Vulnerable employment (% of employees)"                                                
[56] "Early marriage (% of women)"                                                           
[57] "Satisfied demand for contraception (% of women)"                                       
[58] "Corruption (0=high; 100=low)"                                                          
[59] "Acceptance of gays and lesbians (0=low; 100=high)"                                     
[60] "Discrimination and violence against minorities (1=low; 10=high)"                       
[61] "Equality of political power by gender (0=unequal power; 4=equal power)"                
[62] "Equality of political power by socioeconomic position (0=unequal power; 4=equal power)"
[63] "Equality of political power by social group (0=unequal power; 4=equal power)"          
[64] "Years of tertiary schooling"                                                           
[65] "Womens average years in school"                                                        
[66] "Globally ranked universities (points)"                                                 
[67] "Percent of tertiary students enrolled in globally ranked universities"

Oppgave 3: Løsning SSI

Du finner originaldata til oppgaven her: TS Köln

df_ssi <- read_excel("C:/Users/ama123/OneDrive - UiT Office 365/Data/Undervisning/sok2011/data/ssi_2010_2016.xlsx")
df_ssi_sub <- df_ssi  %>% arrange(country, year) %>%
  filter(country=="United States"|country=="Norway"|country=="China" |country=="Russia"|country=="Brazil"|country=="Nigeria")


p_ssi_hwb <- ggplot(data=df_ssi_sub,aes(x=year,y=hwb, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="SSI - Human wellbeing")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, hwb, fill = factor(hwb), label = sprintf('%6.2g', hwb)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, hwb, fill = factor(hwb), label = sprintf('%6.2g', hwb)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c( 2006, 2008, 2010, 2012, 2014, 2016))+
  theme_classic()

p_ssi_nwb <- ggplot(data=df_ssi_sub,aes(x=year,y=nwb, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="SSI - Environmental wellbeing")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, nwb, fill = factor(nwb), label = sprintf('%6.2g', nwb)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, nwb, fill = factor(nwb), label = sprintf('%6.2g', nwb)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c( 2006, 2008, 2010, 2012, 2014, 2016))+
  theme_classic()

p_ssi_ewb <- ggplot(data=df_ssi_sub,aes(x=year,y=ewb, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="SSI - Economic wellbeing")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, ewb, fill = factor(ewb), label = sprintf('%6.2g', ewb)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_ssi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, ewb, fill = factor(ewb), label = sprintf('%6.2g', ewb)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c( 2006, 2008, 2010, 2012, 2014, 2016))+
  theme_classic()

p_ssi_nwb

p_ssi_hwb

p_ssi_ewb

Oppgave 4 - PHDI og IHDI

Data til PHDI og IHDI kommer fra UNDP.

\[ PHDI = HDI\cdot\frac{(CO_{2,Index} + MaterialFootprint_{Index})}{2} \] \[ IHDI = HDI\cdot \frac{(IneqLifeExp_{Index} + IneqEducation_{Index}+ IneqIncome_{Index})}{3} \]

Dere finner en beskrivelse av HDI justert for klima og miljø her:
Dere finner en beskrivelse av HDI justert for ulikhet her:

Tverrsnittsdata:

Variabler

# datasett phdi
# hdi_rank: human development index - country rank
# hdi: human development index
# phdi: planetary pressures adjusted hdi
# diff_phdi: difference phid hdi in percent
# diff_hdi_rank: difference in country rank phid hdi
# p_adj: adjustment factor for planetary pressures
# co_ton: CO2 emissions (tonnes)
# co_index: CO2 emissions index
# mfot_perc: material footprint (tonnes)
# mfoot_index: material footprint index

#datasett ihdi
# ihdi: inequality adjusted hdi
# diff_ihdi: difference ihid hdi in percent
# diff_idi_rank: difference in country rank ihid hdi
# i_adj: adjustment factor for inequality
# i_life_perc: Inequality in life expectancy (%)
# i_life_index: Inequality in life expectancy index
# i_educ_perc: Inequality in education (%)
# i_educ_index: Inequality in education index
# i_inc_perc: Inequality in income (%)
# i_inc_index: Inequality in income index
# inc_40: Income share held by poorest 40% 2010-2021
# Inc_10: Income share held by richest 10% 2010-2021
# inc_1: Income share held by riches 1% 2010-2021
# gini: Gini coefficient 2010-2021

Paneldata

# hdi: human development index
# phdi: planetary pressures adjusted hdi
# ihdi: inequality adjusted hdi

Oppgave 4 - PHDI og IHDI

Visualiser relasjonen mellom 1) HDI og HDI justert for klima og miljø, og 2) HDI og HDI justert for ulikhet, (+ evt relasjonen mellom de to justerte målene)

Data-hjelp (tverrsnitt):

phdi <- read_excel("C:/Users/ama123/OneDrive - UiT Office 365/Data/Undervisning/sok2011/data/HDI_undp/phdi.xlsx")
ihdi <- read_excel("C:/Users/ama123/OneDrive - UiT Office 365/Data/Undervisning/sok2011/data/HDI_undp/ihdi.xlsx")

phdi <-  phdi %>% mutate_all(na_if,"..")
phdi$iso3c<-countryname(phdi$country, destination = 'iso3c') # Legg til kode til alle land (kan brukes til grafisk framstilling)

phdi = phdi %>%  
  mutate(hdi = as.numeric(hdi)) %>% 
  mutate(phdi = as.numeric(phdi))%>% 
  mutate(diff_phdi = as.numeric(diff_phdi))

ihdi <-  ihdi %>% mutate_all(na_if,"..")
ihdi$iso3c<-countryname(ihdi$country, destination = 'iso3c')


ihdi = ihdi %>%  
  mutate(hdi = as.numeric(hdi)) %>% 
  mutate(ihdi = as.numeric(ihdi))%>% 
  mutate(diff_ihdi = as.numeric(diff_ihdi))

df_pihdi <- left_join(phdi, ihdi, by=c('iso3c','country', 'hdi', 'hdi_rank', 'dev_level')) #Sett sammen de to datasettene, slik at vi kan sammenligne dem

df_pihdi$pihdi<-df_pihdi$hdi - ((df_pihdi$diff_phdi)/100)*(df_pihdi$hdi) - ((df_pihdi$diff_ihdi)/100)*(df_pihdi$hdi) # lage et instrument som korrigerer bC%de for ulikhet og miljC8.

Data-hjelp (Paneldata):

Data-materialet er organisert i “wide-format” (som et tverrsnitt) og inneholder en stor mengde variabler. Vi ønsker å endre format på data slik at vi får en variabel som måler “År” og koble våre observasjoner på HDI, PHDI og IHDI til dette.

hdi_orig <- read_csv("C:/Users/ama123/OneDrive - UiT Office 365/Data/Undervisning/sok2011/data/HDI_undp/HDR21-22_Composite_indices_complete_time_series.csv")

# 1. Justere data knyttet til HDI
df_hdi <- hdi_orig %>%
  select(iso3, country, starts_with("hdi")) %>% # Velg ut relevante variabler
  select(-c(hdi_rank_2021, hdicode, starts_with("hdi_f"), starts_with("hdi_m"))) %>% # Ta vekk variabler som har med "hdi" i navnet, men som ikke skal brukes. 
  pivot_longer(             # Endre format fra wide (tverrsnitt) til long (panel)
    cols=-c(iso3, country), 
    names_sep = "_",
    names_to = c(NA, "year"), 
    values_to = "hdi") %>%
mutate(year = as.numeric(year))

# Samme prosedyre for C% lage et panel for phdi
df_phdi <- hdi_orig %>%
  select(iso3, country, starts_with("phdi")) %>%
  pivot_longer(
    cols=-c(iso3, country),
    names_sep = "_",
    names_to = c(NA, "year"), 
    values_to = "phdi") %>%
  mutate(year = as.numeric(year))

# Samme prosedyre for C% lage et panel for ihdi
df_ihdi <- hdi_orig %>%
  select(iso3, country, starts_with("ihdi")) %>%
  pivot_longer(
    cols=-c(iso3, country),
    names_sep = "_",
    names_to = c(NA, "year"), 
    values_to = "ihdi") %>%
  mutate(year = as.numeric(year))


# velg ut land, og koble data til hdi data (dersom du C8nsker C% sammenligne hdi og de to justerte mC%lene)
df_phdi_sub <- df_phdi  %>% 
  left_join(df_hdi, df_phdi, by=c("country", "iso3", "year")) %>% 
  filter(iso3=="ZZK.WORLD" |iso3=="USA"|iso3=="NOR"|iso3=="CHN" |iso3=="RUS"|iso3=="BRA"|iso3=="NGA")


df_ihdi_sub <- df_ihdi  %>% 
  left_join(df_hdi, df_ihdi, by=c("country", "iso3", "year")) %>% 
  filter(year>=2010) %>% 
  filter(iso3=="ZZK.WORLD" |iso3=="USA"|iso3=="NOR"|iso3=="CHN" |iso3=="RUS"|iso3=="BRA"|iso3=="NGA")

Oppgave 4: Løsning

Paneldata

p_ihdi_t<- ggplot(data=df_ihdi_sub,aes(x=year,y=ihdi, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="HDI adjusted for inequality")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_ihdi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, ihdi, fill = factor(ihdi), label = sprintf('%6.2g', ihdi)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_ihdi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, ihdi, fill = factor(ihdi), label = sprintf('%6.2g', ihdi)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c(2010, 2012, 2014, 2016, 2018, 2020))+
  theme_classic()

p_ihdi_t

p_phdi_t<- ggplot(data=df_phdi_sub,aes(x=year,y=phdi, group=country, color=country)) + 
  geom_line(aes(group=country), size=1) +
  geom_point(size=2.5)+
  labs(x = "År" , y ="HDI adjusted for planetary pressures")  +
  theme(legend.position="none")+
  geom_label_repel(
    data=df_phdi_sub %>% group_by(country) %>%
      filter(year==max(year)),
    aes(year, phdi, fill = factor(ihdi), label = sprintf('%6.2g', phdi)),
    color = "black",
    fill = "white")+
  geom_label_repel(
    data=df_phdi_sub %>% group_by(country) %>%
      filter(year==min(year)),
    aes(year, phdi, fill = factor(phdi), label = sprintf('%6.2g', phdi)),
    color = "black",
    fill = "white")+
  scale_x_continuous(breaks=c(1990, 1995, 2000, 2005, 2010, 2015, 2020))+
  theme_classic()

p_phdi_t

Tverrsnitsdata (sammenligning HDI og justerte måleinstrumenter

p_phdi <- phdi %>%
  ggplot(aes(x = hdi, y = phdi)) +
  ylab("HDI adjusted for planetary pressures") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_y_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  geom_abline(intercept = 0, slope = 1, size = 1, color="red") +
  theme_classic()


p_d_phdi<- phdi %>%
  ggplot(aes(x = hdi, y = diff_phdi)) +
  ylab("% adjustment for planetary pressure") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  theme_classic()

p_ihdi<- ihdi %>%
  ggplot(aes(x = hdi, y = ihdi)) +
  ylab("HDI adjusted for inequality") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_y_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  geom_abline(intercept = 0, slope = 1, size = 1, color="red") +
  expand_limits(x = 0.2, y = 0.2) +
  theme_classic()


p_d_ihdi<- ihdi %>%
  ggplot(aes(x = hdi, y = diff_ihdi)) +
  ylab("% adjustment for inequality") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  expand_limits(x = 0.2, y = 0.2) +
  theme_classic()



p_pihdi<- df_pihdi %>%
  ggplot(aes(x = hdi, y = pihdi)) +
  ylab("HDI adjusted for planetary pressures, and inequality") + 
  xlab("Human development index") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
 scale_y_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  geom_abline(intercept = 0, slope = 1, size = 1, color="red") +
  theme_classic()


p_phdi_ihdi<- df_pihdi %>%
  ggplot(aes(x = ihdi, y = phdi)) +
  ylab("HDI adjusted for planetary pressures") + 
  xlab("HDI adjusted for inequality") + 
  geom_text(aes(label=iso3c),size=3)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = 'white'))+
  scale_y_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  scale_x_continuous(breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0))+
  geom_abline(intercept = 0, slope = 1, size = 1, color="red") +
  expand_limits(x = 0.2, y = 0.2) +
  theme_classic()

p_phdi

p_ihdi

p_d_phdi

p_d_ihdi

p_pihdi

p_phdi_ihdi

Oppgave 5

Sammenlign resultatene fra din grafiske analyse.

Hvordan skal vi tolke de enkelte grafene?
Hvilket av måleinstrumentene syns du er “best” og hvorfor? Diskuter fordeler og ulemper med å evaluere bærkraftig utvikling ved bruk av de ulike måleinstrumentene.

p_gsi

p_spi

Sustainable society index

p_ssi_hwb

p_ssi_nwb

p_ssi_ewb

HDI

p_phdi_t

p_ihdi_t

p_phdi

p_ihdi

p_d_phdi

p_d_ihdi

p_pihdi

p_phdi_ihdi